Rare variants contribute to schizophrenia risk, including gene disrupting large insertions and deletions and single nucleotide variants? however, these mutations and the causal genes they disrupt have proven difficult to pinpoint and characterize, and their effect sizes are estimated across broad classes of variants and gene sets. New data from next generation DNA sequencing applications enable rare variant detection and association. We propose to develop statistical models to infer causal genes and variants, and their effects on schizophrenia risk, from large scale exome sequencing data. Schizophrenia is a common, complex psychiatric disorder that affects as much as 1% of the population, over two million people in the United States. Schizophrenia comes with debilitating comorbidities ranging from unemployment to early death, and there is no cure, only palliative treatment with moderate success rates. Development of genomic models for clinical risk prediction of common, complex disease risk is a burgeoning area of research, with long-term potential for